Arnovitz, S. | Search bioRxiv

bioRxiv2024

A modular system for programming multistep activation of endogenous genes in stem cells

Although genomes encode instructions for mammalian cell differentiation with rich syntactic relationships, existing methods for genetically programming cells have modest capabilities for stepwise regulation of genes. Here, we developed a sequential genetic system that enables transcriptional activation of endogenous genes in a preprogrammed, stepwise manner. The system relies on the removal of an RNA polymerase III termination signal to induce both the transcriptional activation and the DNA endonuclease activities of a Cas9-VPR protein to effect stepwise progression through cascades of gene activation events. The efficiency of the cascading system enables a new dimension for cellular programming by allowing the manipulation of the sequential order of gene activation for directing the differentiation of human stem cells. One-Sentence SummaryDevelopment of a synthetic biology system for preprogrammed, stepwise activation of endogenous genes.

synthetic biology↗

bioRxiv2020

FiberAI: A Deep Learning model for automated analysis of nascent DNA Fibers

All life forms undergo cell division and are dependent on faithful DNA replication to maintain the stability of their genomes. Both intrinsic and extrinsic factors can stress the replication process and multiple checkpoint mechanisms have evolved to ensure genome stability. Understanding these molecular mechanisms is crucial for preventing and treating genomic instability associated diseases including cancer. DNA replicating fiber fluorography is a powerful technique that directly visualizes the replication process and a cells response to replication stress. Analysis of DNA-fiber microscopy images provides quantitative information about replication fitness. However, a bottleneck for high throughput DNA-fiber studies is that quantitative measurements are laborious when performed manually. Here we introduce FiberAI, which uses state-of-the art deep learning frameworks to detect and quantify DNA-fibers in high throughput microscopy images. FiberAI efficiently detects DNA fibers, achieving a bounding box average precision score of 0.91 and a segmentation average precision score of 0.90. We then use FiberAI to measure the integrity of replication checkpoints. FiberAI is publicly available and allows users to view model predicted selections, add their own manual selections, and easily analyze multiple image sets. Thus, FiberAI can help elucidate DNA replication processes by streamlining DNA-fiber analyses.

bioinformatics↗